Vectors are key to how AI models represent data semantics. Searching for vectors has now become a key requirement of databases in order to facilitate AI first applications. MariaDB Server will introduce Vector similarity search in an upcoming release. In this talk we will discuss what MariaDB vector is, how it works behind the scenes, as well as possible use cases and future roadmap.
MariaDB Vector introduces a new high-level interface for indexing within MariaDB Server. This interface allows one to create custom indexing strategies. Vector search requires a special kind of index.
The algorithm that is used by MariaDB and many other vector databases is called Hierarchical Navigable Small Worlds (HNSW). In this talk we will focus on the basis of HNSW, why the algorithm returns approximate results and what impacts its performance (tuning parameters).
We will also discuss the difference between a Generative AI model and an Embedding AI model and how they can be used to build Retrieval Augmented Generation applications using MariaDB as a datastore.
Finally we'll describe the current eco-system of Vector databases what are their strengths and weaknesses. With the information provided, one will be able to make a more informed decision between choosing a dedicated Vector database, or stick with a traditional Relational Database with vector search support.